Model Statistics for R/Weka Classifiers
Compute model performance statistics for a fitted Weka classifier.
evaluate_Weka_classifier(object, newdata = NULL, cost = NULL, numFolds = 0, complexity = FALSE, class = FALSE, seed = NULL, ...)
- an optional data frame in which to look for variables
with which to evaluate. If omitted or
NULL, the training instances are used.
- a square matrix of (mis)classification costs.
- the number of folds to use in cross-validation.
- option to include entropy-based statistics.
- option to include class statistics.
- optional seed for cross-validation.
- further arguments passed to other methods (see details).
The function computes and extracts a non-redundant set of performance statistics that is suitable for model interpretation. By default the statistics are computed on the training data.
... only supports the logical variable
normalize which tells Weka to normalize the cost matrix so that
the cost of a correct classification is zero.
Note that if the class variable is numeric only a subset of the statistics
are available. Arguments
class are then
not applicable and therefore ignored.
- An object of class
Weka_classifier_evaluation, a list of the following components:
string character, concatenation of the string representations of the performance statistics. details vector, base statistics, e.g., the percentage of instances correctly classified, etc. detailsComplexity vector, entropy-based statistics (if selected). detailsClass matrix, class statistics, e.g., the true positive rate, etc., for each level of the response variable (if selected). confusionMatrix table, cross-classification of true and predicted classes.
I. H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
## Use some example data. w <- read.arff(system.file("arff","weather.nominal.arff", package = "RWeka")) ## Identify a decision tree. m <- J48(play~., data = w) m ## Use 10 fold cross-validation. e <- evaluate_Weka_classifier(m, cost = matrix(c(0,2,1,0), ncol = 2), numFolds = 10, complexity = TRUE, seed = 123, class = TRUE) e summary(e) e$details